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MarketPulse MCP

MarketPulse MCP

by tanmaymish
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_stock_signal_score

Combine multi-agent consensus, smart money, social sentiment, and pledge risk into a 0-100 BUY/HOLD/SELL score for automated stock ranking.

Instructions

Automation-friendly stock ranking score built from FinStack signals.

Combines:

  • multi-agent consensus

  • smart money activity

  • social sentiment

  • promoter pledge risk

  • insider signal

  • technical momentum

  • peer / sector context

  • earnings setup

Returns:

  • signal_score: 0-100

  • signal: BUY / HOLD / SELL

  • automation_rank

  • top_supports / top_risks

  • full component breakdown

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "symbol": "<symbol>"
    },
    "name": "get_stock_signal_score"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.10.0

TDQS

B3.3/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It transparently lists the signal inputs and return fields, including a full component breakdown. However, it omits behavioral details such as data recency, latency, or interpretation of 'automation_rank', leaving some ambiguity about what the score truly reflects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with separate bullet lists for components and returns, and every line adds useful information. It is slightly long but remains efficient and easy to scan for an agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose, inputs, and output fields, and an output schema exists so return documentation is adequate. It lacks a brief note on interpreting the 0-100 signal_score, but overall it provides enough context for a straightforward composite score tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has one required parameter (symbol) with 0% description coverage, and the description offers no additional context about symbol format, exchange, or examples. Since the schema is minimal, the description should compensate but does not, making parameter semantics weak.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as an 'Automation-friendly stock ranking score built from FinStack signals' and lists the signal components and return fields, making its purpose specific. It distinguishes itself from more granular siblings by referencing a composite score, though it does not explicitly contrast with alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies a consolidated-view use case by listing eight signal categories, but it never states when to choose this tool over individual signal tools like get_social_sentiment or get_insider_signal. The 'automation-friendly' phrase hints at programmatic use but offers no explicit when-to-use or when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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